Multi-input Multi-output Systems Research Articles

Optimized beamforming vector and antenna selection using autoencoder with Bayesian RNN and hybrid heuristic algorithm for MIMO systems

SummaryWireless communication systems depend on channel state information (CSI). However, the CSI collection is a significant barrier to achieving high performance. Present wireless communication models mainly rely on many antennas and related signal dispensation for increasing effectiveness. User mobility, the usage of greater frequencies, and a better number of antennas contribute to beamforming and beam management in these networks. Therefore, an improved hybrid heuristic algorithm is proposed here for providing optimized Joint beam forming and antenna selection (JBAS) in a multi‐input multi‐output (MIMO) model. The major novelty of the research work is to jointly perform the beam forming and antenna selection in MIMO that effectively enhanced the performance of the wireless transmission system. The major intention of this paper is to optimize the beamforming vectors and antennae by using the designed algorithm, where the spectral efficiency gets maximized. Initially, the data is collected by varying the number of users, locations, and sampling points, which is optimally selected by developing the hybrid reptile search‐based lion optimization algorithm (HRS‐LOA). Once it is collected, the relevant CSI is considered. This state of information is fed as input to the Auto Encoder with Bayesian Recurrent Neural Network (AE‐BRNN). Finally, through the network, the optimized value of the beamforming vector and antenna is obtained, which is useful to increase the sum rate/spectral efficiency. From the numerical results, the recommended model attained a spectral efficiency of 30 bits/s/Hz, which is higher than the BWO‐AE‐BRNN with 13%, RSO‐AE‐BRNN with 16%, RSA‐AE‐BRNN with 17%, and LA‐AE‐BRNN with 40% respectively.

Decoupled Model-Free Adaptive Control with Prediction Features Experimentally Applied to a Three-Tank System Following Time-Varying Trajectories

In this paper, the performance of three model-free control approaches on a multi-input, multi-output (MIMO) nonlinear system with constant and time-varying references is compared. The first control algorithm is model-free adaptive control (MFAC). The second is a modified version of MFAC (MMFAC) designed to handle delays in the system by incorporating the output error difference (over two sample time steps) in the control input. The third approach, model-free adaptive predictive control (MFAPC) with a one-step-ahead forecast of the system input, is obtained by using predictions of the outputs based on the data-based linear model. The experimental device used is an MIMO three-tank system (3TS) assumed to be an interconnected system with multiple coupled single-input, single-output (SISO) subsystems with unmeasurable couplings. The novelty of this contribution is that each coupled SISO partition is assumed to be controlled independently using a decoupled control algorithm, leading to fewer control parameters compared to a centralized MIMO controller. Additionally, both parameter tuning for each controller and performance evaluation are conducted using an evaluation criterion considering energy consumption and accumulated tracking error. The results demonstrate that almost all the proposed model-free controllers effectively control an MIMO system by controlling its SISO subsystems individually. Moreover, the predictive features in the decoupled MFAPC contribute to more accurate tracking of time-varying references. The utilization of tracking error differences helps in reducing energy consumption.

A low-complexity solution for LMIs of hardly constrained nonlinear MIMO tracking control systems

This research is dedicated to the discrete-time tracking control of nonlinear multi-input multi-output (MIMO) Newtonian mechanics systems with an equal number of inputs and outputs, in the presence of input saturation and high-order dynamics of the actuation system. Firstly, the continuous-time model of the plant and actuation system has been transformed into the discrete-time domain using Adams–Bashforth and Adams–Moulton discretization techniques. In the next step, considering saturation boundaries, the modified position and velocity references are derived using the discrete-time reaching law-based control approach. These modified references are obtained through an optimization algorithm that calculates the closest achievable position and velocity trajectories with respect to the desired reference trajectory. Modifying desired trajectories in MIMO systems results in the formation of a system of linear matrix inequalities (LMIs). Solving such systems of inequalities requires the use of complex methods, and this difficulty increases with the increase in the system’s degrees of freedom (DOFs). The proposed strategy offers a more straightforward approach to solving these inequalities. Once the modified references are calculated, the corresponding position and velocity control signals can be obtained using the mentioned reaching law strategies. The final step of the proposed algorithm is to combine the resulting position and velocity control signals using a Gaussian-weighted averaging method to form the main control signal. A desirable performance for the control system can be achieved by appropriately adjusting the standard deviations of the Gaussian weighing functions. To evaluate the performance characteristics of the proposed method, it has been applied in the tracking control procedure of a nonlinear 3-DOF mechanical system. The numerical simulation results demonstrate the effectiveness of the method.

Impedance modeling and quantitative stability analysis of grid-connected voltage source converters under complex unbalanced conditions

The stability issues caused by voltage source converters (VSCs), which are commonly used in renewable energy systems, are investigated in this paper. Much literature has investigated the impedance models and quantitative stability analysis for converters under the weak and unbalanced grid. However, on the one hand, harmonic-transfer-function (HTF)-based modeling methods are complex, and the established infinite-order models may suffer from unreasonable truncation. On the other hand, these existing impedance models without considering all unbalance factors are inaccurate. Both of these may bring wrong stability analysis results. Therefore, this paper proposes a simple impedance extension method and considers all unbalance factors to derive the impedance model of the converter. However, due to the established higher-order impedance model, the traditional impedance-ratio based stability analysis method is not applicable to multi-input multi-output (MIMO) systems. The commonly used eigenvalue-based GNC and diagonalization-based methods applicable to MIMO systems are cumbersome and not suitable for quantitative analysis. Therefore, in this paper, a quantitative analysis tool of system stability based on the phase-frequency characteristics of determinants is developed. Then, stability regions in the multi-parameter space are obtained. Finally, the established impedance model and the quantitative analysis results are validated via both simulations and hardware experiments.© 2017 Elsevier Inc. All rights reserved.

Suppression of Combustion Oscillations in Hydrogen-Enriched Can-Type Combustors Through Fuel Staging

Abstract To achieve decarbonization in power-generating gas turbines, the technology of mixing hydrogen with natural gas is garnering significant attention. However, when blending natural gas with hydrogen, the altered combustion characteristics can lead to combustion instability in gas turbine combustors. Although fuel staging can effectively suppress combustion instability for can-type combustors, further research on mitigation strategies for hydrogen cofiring and their predictive methods is required. This study involves hydrogen cofiring experiments using a full-scale can-type combustor. Moreover, the resulting suppression of combustion instability is analyzed through fuel staging by utilizing three-dimensional (3D) computational fluid dynamics (CFD) and one-dimensional (1D) thermo-acoustic analysis. The experiments used a full-scale industrial can-type combustor with a five-around-one nozzle configuration. Hydrogen was blended with natural gas up to a volume fraction of 30%, maintaining a constant thermal power. Fuel staging was applied by controlling two out of five outer nozzles (ONs) along with the remaining three. Before the 1D thermo-acoustic analysis, the internal flame structure of the combustor was examined through 3D CFD analysis. Based on the results, a multi-input multi-output (MIMO) system was constructed for 1D thermo-acoustic analysis of the can-type combustor. The application of time delays derived from 3D CFD analysis to the 1D model revealed that differences in flame time delays across the nozzles cause combustion instability suppression observed in fuel staging.

Simultaneous position and force control of a SMA-actuated continuum robotic module

Continuum manipulators with controllable shape and exerting force are attractive devices. Utilizing great features of shape memory alloys (SMAs), thin continuum modules are developed. SMAs are embedded around an elastic rod and activated through a control algorithm to set a desired shape. However, complex behavior of SMAs in such a multi-input multi-output system, make the control challenging. Additionally, simultaneous control of position and the force applied by the module, as a challenging problem, has not been investigated so far. Handling this problem may increase the application of continuum robots when utilized as manipulators or when pass through narrow and complex canals with sensitive wall. In this research, position and force control of such continuum module is under focus for achieving a more practical tool for better non-invasive medical devices. Further to the position control, the amount of force applied to the environment is adjusted in different locations of the workspace through a novel fuzzy controller. The results indicate the possibility of simultaneous control of the position and force using fuzzy controller with a reasonable accuracy of 0.5° for angle and 0.05 N for the force. The concluded results may be utilized in developing smarter soft robots.

DBFact applied to minimum variance performance assessment for nonminimum phase multivariate systems from closed‐loop data

AbstractThis paper introduces an approach for determining a minimum variance control (MVC) benchmark for nonminimum phase (NMP) multi‐input multi‐output (MIMO) systems using closed‐loop operational data. The MVC benchmark is derived from the MVC law of DBFact factorization introduced by Lima, Trierweiler, and Farenzena. Unlike other factorization methods, DBFact offers advantages such as non‐iterative computation and ensuring internal stability of the MVC law. This approach considers the inherent directionality of NMP MIMO systems, enhancing the reliability of the control performance index. However, the original method relies on prior knowledge of the process model. To overcome this limitation, this paper proposes a method for calculating the MVC benchmark when prior knowledge is absent. It introduces a MIMO system identification strategy employing minimally invasive signal tests. The methodology is evaluated across various control conditions using a quadruple‐tank plant with additional time delays. The study emphasizes the importance of directionality in assessing MIMO system performance, particularly in evaluating individual loop performances. Results demonstrate the identification procedure's effectiveness in accurately calculating the proposed MVC benchmark, even with a mere 1% increase in output variance considered.

Fixed-Time Fault-Tolerant Adaptive Neural Network Control for a Twin-Rotor UAV System with Sensor Faults and Disturbances

This paper presents a fixed-time fault-tolerant adaptive neural network control scheme for the Twin-Rotor Multi-Input Multi-Output System (TRMS), which is challenging due to its complex, unstable dynamics and helicopter-like behavior with two degrees of freedom (DOFs). The control objective is to stabilize the TRMS in trajectory tracking in the presence of unknown nonlinear dynamics, external disturbances, and sensor faults. The proposed approach employs the backstepping technique combined with adaptive neural network estimators to achieve fixed-time convergence. The unknown nonlinear functions and disturbances of the system are processed via an adaptive radial basis function neural network (RBFNN), while the sensor faults are actively estimated using robust terms. The developed controller is applied to the TRMS using a decentralized structure where each DOF is controlled independently to simplify the control scheme. Moreover, the parameters of the proposed controller are optimized by the gray-wolf optimization algorithm to ensure high flight performance. The system’s stability analysis is proven using a Lyapunov approach, and simulation results demonstrate the effectiveness of the proposed controller.

An efficient parallel self-attention transformer for CSI feedback

In massive multi-input multi-output (MIMO) systems, it is necessary for user equipment (UE) to transmit downlink channel state information (CSI) back to the base station (BS). As the number of antennas increases, the feedback overhead of CSI consumes a significant amount of uplink bandwidth resources. To minimize the bandwidth overhead, we propose an efficient parallel attention transformer, called EPAformer, a lightweight network that utilizes the transformer architecture and efficient parallel self-attention (EPSA) for CSI feedback tasks. The EPSA expands the attention area of each token within the transformer block effectively by dividing multiple heads into parallel groups and conducting self-attention in horizontal and vertical stripes. The proposed EPSA achieves better feature compression and reconstruction. The simulation results display that the EPAformer surpasses previous deep learning-based approaches in terms of reconstruction performance and complexity.

A compact meta-learned neuro-fuzzy technique for noise-robust nonlinear control

Neuro-fuzzy systems show promise for adaptive control but can become complex due to the need to learn many parameters. This paper presents a resilient nonlinear controller that combines a simplified neuro-fuzzy system (Simp_NFS) and simplified neural network (Simp_NN) with only two meta-learnable parameters. This architecture enables fast and stable adaptation in uncertain nonlinear discrete-time systems. Simp_NFS utilizes interpretable hyperplane-based rules without antecedent parameters, simplifying the learning process to consequent weights. Simp_NN reduces complexity by replacing hidden-output weights with their mean. The hybrid auto-adaptive controller (HAC) combines the advantages of Simp_NFS and Simp_NN, significantly reducing the number of adaptive parameters compared to standard neuro-fuzzy methods for real-time control with limited resources. Simp_NFS provides structural adaptivity to handle uncertainties, while Simp_NN ensures reliable disturbance attenuation. The stability of HAC is proven using Lyapunov analysis. Extensive testing on challenging single-input single-output (SISO) and multi-input multi-output systems (MIMO) demonstrates that HAC improves performance by up to 82.55% compared to existing techniques. Key innovations include an ultra-compact meta-learned architecture, transparent online evolution of hyperplane clusters, and enhanced modeling capability for nonlinear uncertain systems. This interpretable neuro-fuzzy approach could enhance autonomy and safety by maintaining model transparency. The implementation of HAC is publicly available on GitHub at https://github.com/m-ferdaus/HAC.

Robust PI-PD Controller Design: Industrial Simulation Case Studies and a Real-Time Application

PI-PD controllers have superior performance compared to traditional PID controllers, especially for controlling unstable and integrating industrial processes with time delays. However, computing the four tuning parameters of this type of controller is not an easy task. Recently, there has been significant interest in determining the tuning rules for PI-PD controllers that utilize the stability region. Currently, most tuning rules for the PI-PD controller are presented graphically, which can be time-consuming and act as a barrier to their industrial application. There is a lack of analytical tuning guidelines in the literature to address this shortfall. However, the existing analytical tuning guidelines do not consider a rigorous design approach. This work proposes new robust analytical tuning criteria based on predefined gain and phase margin bounds, as well as the centroid of the stability region. The proposed method has been tested using various simulation studies related to a DC–DC buck converter, a DC motor, and a heat exchanger. The results indicate that the proposed tuning rules exhibit strong performance against parameter uncertainty with minimal overshoots. Furthermore, the suggested technique for simultaneous control of yaw and pitch angles has been tested in a real-time application using the twin rotor multi-input multi-output system (TRMS). Real-time results indicate that, compared to other methods under investigation, the suggested approach provides nearly minimal overshoots.

Metaheuristic Algorithm-Based Proportional–Integrative–Derivative Control of a Twin Rotor Multi Input Multi Output System

Metaheuristic algorithms are computational techniques based on the collective behavior of swarms and the study of organisms acting in communities. These algorithms involve different types of organisms. Finding controller values for nonlinear systems is a challenging task using classical approaches. Hence, using metaheuristics to find the controller values of a twin rotor multi-input multi-output system (TRMS), one of the nonlinear systems studied in the literature, seems to be more appropriate than using classical methods. In this study, different types of metaheuristic algorithms were used to find the PID controller values for a TRMS, including a genetic algorithm (GA), a dragonfly algorithm, a cuckoo algorithm, a particle swarm optimization (PSO) algorithm, and a coronavirus optimization algorithm (COVIDOA). The obtained graphs were analyzed based on certain criteria for the main rotor and tail rotor angles to reach the reference value in the TRMS. The experimental results show that when the rise and settlement times of the TRMS are compared in terms of performance, the GA took 1.5040 s (seconds) and the COVIDOA took 9.59 s to increase the pitch angle to the reference value, with the GA taking 0.7845 s and the COVIDOA taking 2.4950 s to increase the yaw angle to the reference value. For the settling time, the GA took 11.67 s and the COVIDOA took 28.01 s for the pitch angle, while the GA took 14.97 s and the COVIDOA took 26.69 s for the yaw angle. With these values, the GA and COVIDOA emerge as the foremost algorithms in this context.

A decentralized optimal PID controller with disk margin-based robust stability analysis for higher-order industrial systems

ABSTRACT Decentralized control systems are frequently employed to tackle control challenges in multi-input multi-output (MIMO) systems. Hence, this paper proposes a decentralized PID controller that relies on frequency domain specifications. This controller is designed to minimize the loop interactions and fulfill the design criteria of the MIMO system. The PID parameters are calculated based on gain and phase margin specifications. Additionally, a first-order plus dead time model is derived for each of the decoupled subsystems. Furthermore, robust stability is analysed using disc margin analysis, which overcomes the constraints of traditional margins. Moreover, the paper determines a secure range of uncertainty for various levels of gain and phase uncertainties. The findings suggest that the proposed controller achieves robust stability over a broader range of margins when compared to alternative control methods.

Ultra-Fast Nonlinear Model Predictive Control for Motion Control of Autonomous Light Motor Vehicles

Advanced Driver Assistance System (ADAS) is the latest buzzword in the automotive industry aimed at reducing human errors and enhancing safety. In ADAS systems, the choice of control strategy is not straightforward due to the highly complex nonlinear dynamics, control objectives, and safety critical constraints. Nonlinear Model Predictive Control (NMPC) has evolved as a favorite option for optimal control due to its ability to handle such constrained, Multi-Input Multi-Output (MIMO) systems efficiently. However, NMPC suffers from a bottleneck of high computational complexity, making it unsuitable for fast real-time applications. This paper presents a generic framework using Successive Online Linearization-based NMPC (SOL-NMPC) for for the control in ADAS. The nonlinear system is linearized and solved using Linear Model Predictive Control every iteration. Furthermore, offset-free MPC is developed with the Extended Kalman Filter for reducing model mismatch. The developed SOL-NMPC is validated using the 14-Degrees-of-Freedom (DoF) model of a D-class light motor vehicle. The performance is simulated in matlab/Simulink and validated using the CarSim® software (Version 2016). The real-time implementation of the proposed strategy is tested in the Hardware-In-the-Loop (HIL) co-simulation using the STM32-Nucleo-144 development board. The detailed performance analysis is presented along with time profiling. It can be seen that the loss of accuracy can be counteracted by the fast response of the proposed framework.

Model reference adaptive controller with interpretable fuzzy rules for linear MIMO systems

This paper presents a method for fuzzy model reference adaptive control system design for a partially known multi-input multi-output (MIMO) linear dynamic plant. Differential or convolutional equations can describe the plant to be controlled, and the fuzzy controller is assumed to be from the MIMO sector-bounded nonstationary nonlinearities class. The task is to obtain robust adaptive stabilization. Some absolute stability theorems and integral evaluations of the state and control vectors are applied in the MIMO fuzzy adaptive controller design procedure for the first time. A method for automatically obtaining a near-optimal nonlinear dynamic reference model is described. A practical step-by-step procedure of the MIMO adaptive fuzzy controller design is proposed, resulting in highly interpretable MIMO fuzzy control rules based on triangular fuzzy sets and strong triangular fuzzy partition. An example of the fuzzy controller design according to the proposed procedure is given.

Uncertainty disturbance estimator control for delayed linear systems with input constraint

This paper proposes a novel Uncertainty and Disturbance Estimator (UDE) based controller for Multi-Input Multi-Output (MIMO) systems. The controller handles input constraints, time-varying matched uncertainties in state and input matrices, unknown input–output delays, and partial state measurement. The UDE error dynamics are augmented with a time-varying variable to handle input constraints effectively. The proposed controller employs a Proportional Integral Observer (PIO) and a state predictor to accurately estimate the future state of the system with only partial state measurement. We develop sufficient conditions for system stability in tracking a reference model while only an estimation of the total delay is available. Numerical simulations and experiments on a multi-rotor platform validate the proposed controller’s effectiveness, demonstrating improved tracking performance and stability compared to existing methods. The proposed UDE-based controller, with a PIO and state predictor, is capable of handling various matched uncertainties and disturbances in MIMO systems with input constraints and partial state measurement. It offers improved tracking performance and stability compared to existing studies.

Nonlinear Decoupling Control With PIλ Dμ Neural Network for MIMO Systems.

In this brief, a fractional order proportional-integral-differential neural network (PIDNN) controller based on the beetle swarm optimization algorithm (BSO-PI [Formula: see text]NN) is proposed for multi-input multi-output (MIMO) systems with strong coupling. First, the fractional order PID operator is introduced to the hidden layer neurons of the neural network, where long memory characteristics of the fractional order neurons can improve the control accuracy and convergence speed. Second, a sufficient condition on the learning rate is established to ensure the stability of the controller by the Lyapunov theory. Third, the PI [Formula: see text]NN is initialized by the BSO algorithm to prevent weights from falling into local optima. The proposed fractional order PIDNN controller can eliminate the coupling between variables and achieve desirable control performance without specific system models. To the authors' best knowledge, this is the first work that the fractional order PI [Formula: see text] neurons are employed in neural network. Two simulation examples verify the effectiveness and superiority of the proposed controller.

Dynamic feature-based deep reinforcement learning for flow control of circular cylinder with sparse surface pressure sensing

This study proposes a self-learning algorithm for closed-loop cylinder wake control targeting lower drag and lower lift fluctuations with the additional challenge of sparse sensor information, taking deep reinforcement learning (DRL) as the starting point. The DRL performance is significantly improved by lifting the sensor signals to dynamic features (DFs), which predict future flow states. The resulting DF-based DRL (DF-DRL) automatically learns a feedback control in the plant without a dynamic model. Results show that the drag coefficient of the DF-DRL model is 25 % less than the vanilla model based on direct sensor feedback. More importantly, using only one surface pressure sensor, DF-DRL can reduce the drag coefficient to a state-of-the-art performance of approximately 8 % at Reynolds number $(Re) = 100$ and significantly mitigates lift coefficient fluctuations. Hence, DF-DRL allows the deployment of sparse sensing of the flow without degrading the control performance. This method also exhibits strong robustness in flow control under more complex flow scenarios, reducing the drag coefficient by 32.2 % and 46.55 % at $Re =500$ and 1000, respectively. Additionally, the drag coefficient decreases by 28.6 % in a three-dimensional turbulent flow at $Re =10\,000$ . Since surface pressure information is more straightforward to measure in realistic scenarios than flow velocity information, this study provides a valuable reference for experimentally designing the active flow control of a circular cylinder based on wall pressure signals, which is an essential step toward further developing intelligent control in a realistic multi-input multi-output system.

Development of a Universal Adaptive Control Algorithm for an Unknown MIMO System Using Recursive Least Squares and Parameter Self-Tuning

Development of a Universal Adaptive Control Algorithm for an Unknown MIMO System Using Recursive Least Squares and Parameter Self-Tuning

Neuroadaptive Fault-Tolerant Control Embedded With Diversified Activating Functions With Application to Auto-Driving Vehicles Under Fading Actuation.

This article presents a neuroadaptive fault-tolerant control method for path tracking of multiinput multioutput (MIMO) systems in the presence of modeling uncertainties and external disturbances. In dealing with modeling uncertainties, neural networks (NNs) with diversified activation/basis functions are considered, with which we establish a set of control algorithms that are robust against uncertainties, adaptive to unknown parameters, and tolerant to actuation faults. This is the first work that explicitly takes into account the neural weights uncertainties and activating function uncertainties in multiple layered neural networks in control design. In addition, we apply the developed control algorithms to unmanned ground vehicles (UGVs) with actuator failures. With the aid of Lyapunov stability theory, it is shown that the proposed control is able to drive the vehicle along the desired path with high precision and all the internal signals are uniformly ultimately bounded (UUB) and continuous. Both theoretical analysis and numerical simulation confirm the effectiveness of the designed strategy.